From b0217f655cdc7491f6398821242dd3a3165f5be8 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 14:45:13 +0000 Subject: automatic import of python-caserecommender --- .gitignore | 1 + python-caserecommender.spec | 319 ++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 321 insertions(+) create mode 100644 python-caserecommender.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..cec6fda 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/CaseRecommender-1.1.1.tar.gz diff --git a/python-caserecommender.spec b/python-caserecommender.spec new file mode 100644 index 0000000..3df4c59 --- /dev/null +++ b/python-caserecommender.spec @@ -0,0 +1,319 @@ +%global _empty_manifest_terminate_build 0 +Name: python-CaseRecommender +Version: 1.1.1 +Release: 1 +Summary: A recommender systems framework for Python +License: MIT License +URL: https://github.com/caserec/CaseRecommender +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4d/2c/c3652c9da575c89fc8c5f8d281d6e1997a09376ee3c22f1f4581fe5d08a2/CaseRecommender-1.1.1.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-pandas + +%description +Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and +explicit feedback. The framework aims to provide a rich set of components from which you can construct a customized +recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating +prediction approaches, and different metrics validation and evaluation. +Algorithms +^^^^^^^^^^^^ +Item Recommendation: +- BPRMF +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Group-based (Clustering-based algorithm) +- Paco Recommender (Co-Clustering-based algorithm) +- Most Popular +- Random +- Content Based +Rating Prediction: +- Matrix Factorization (with and without baseline) +- SVD +- Non-negative Matrix Factorization +- SVD++ +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Item NSVD1 (with and without Batch) +- User NSVD1 (with and without Batch) +- Most Popular +- Random +- gSVD++ +- Item-MSMF +- (E)CoRec +Clustering: +- PaCo: EntroPy Anomalies in Co-Clustering +- k-medoids +Evaluation and Validation Metrics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- All-but-one Protocol +- Cross-fold- Validation +- Item Recommendation: Precision, Recall, NDCG and Map +- Rating Prediction: MAE and RMSE +- Statistical Analysis (T-test and Wilcoxon) +Requirements +^^^^^^^^^^^^^ +- Python >= 3 +- scipy +- numpy +- pandas +- scikit-learn +For Linux, Windows and MAC use: + $ pip install requirements +For Windows libraries help use: + http://www.lfd.uci.edu/~gohlke/pythonlibs/ +Quick Start and Guide +^^^^^^^^^^^^^^^^^^^^^^ +For more information about RiVal and the documentation, +visit the Case Recommender +`Wiki `_. If you have not used Case Recommender before, do check out the Getting Started guide. +Installation +^^^^^^^^^^^^^ +Case Recommender can be installed using pip: + $ pip install caserecommender +If you want to run the latest version of the code, you can install from git: + $ pip install -U git+git://github.com/caserec/CaseRecommender.git +More Details +^^^^^^^^^^^^^ + `https://github.com/caserec/CaseRecommender `_ +License (MIT) +^^^^^^^^^^^^^^ + © 2019. Case Recommender All Rights Reserved + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated + documentation files (the "Software"), to deal in the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to the following conditions: + The above copyright notice and this permission notice shall be included in all copies or substantial portions + of the Software. + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED + TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION + OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS IN THE SOFTWARE. + +%package -n python3-CaseRecommender +Summary: A recommender systems framework for Python +Provides: python-CaseRecommender +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-CaseRecommender +Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and +explicit feedback. The framework aims to provide a rich set of components from which you can construct a customized +recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating +prediction approaches, and different metrics validation and evaluation. +Algorithms +^^^^^^^^^^^^ +Item Recommendation: +- BPRMF +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Group-based (Clustering-based algorithm) +- Paco Recommender (Co-Clustering-based algorithm) +- Most Popular +- Random +- Content Based +Rating Prediction: +- Matrix Factorization (with and without baseline) +- SVD +- Non-negative Matrix Factorization +- SVD++ +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Item NSVD1 (with and without Batch) +- User NSVD1 (with and without Batch) +- Most Popular +- Random +- gSVD++ +- Item-MSMF +- (E)CoRec +Clustering: +- PaCo: EntroPy Anomalies in Co-Clustering +- k-medoids +Evaluation and Validation Metrics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- All-but-one Protocol +- Cross-fold- Validation +- Item Recommendation: Precision, Recall, NDCG and Map +- Rating Prediction: MAE and RMSE +- Statistical Analysis (T-test and Wilcoxon) +Requirements +^^^^^^^^^^^^^ +- Python >= 3 +- scipy +- numpy +- pandas +- scikit-learn +For Linux, Windows and MAC use: + $ pip install requirements +For Windows libraries help use: + http://www.lfd.uci.edu/~gohlke/pythonlibs/ +Quick Start and Guide +^^^^^^^^^^^^^^^^^^^^^^ +For more information about RiVal and the documentation, +visit the Case Recommender +`Wiki `_. If you have not used Case Recommender before, do check out the Getting Started guide. +Installation +^^^^^^^^^^^^^ +Case Recommender can be installed using pip: + $ pip install caserecommender +If you want to run the latest version of the code, you can install from git: + $ pip install -U git+git://github.com/caserec/CaseRecommender.git +More Details +^^^^^^^^^^^^^ + `https://github.com/caserec/CaseRecommender `_ +License (MIT) +^^^^^^^^^^^^^^ + © 2019. Case Recommender All Rights Reserved + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated + documentation files (the "Software"), to deal in the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to the following conditions: + The above copyright notice and this permission notice shall be included in all copies or substantial portions + of the Software. + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED + TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION + OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS IN THE SOFTWARE. + +%package help +Summary: Development documents and examples for CaseRecommender +Provides: python3-CaseRecommender-doc +%description help +Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and +explicit feedback. The framework aims to provide a rich set of components from which you can construct a customized +recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating +prediction approaches, and different metrics validation and evaluation. +Algorithms +^^^^^^^^^^^^ +Item Recommendation: +- BPRMF +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Group-based (Clustering-based algorithm) +- Paco Recommender (Co-Clustering-based algorithm) +- Most Popular +- Random +- Content Based +Rating Prediction: +- Matrix Factorization (with and without baseline) +- SVD +- Non-negative Matrix Factorization +- SVD++ +- ItemKNN +- Item Attribute KNN +- UserKNN +- User Attribute KNN +- Item NSVD1 (with and without Batch) +- User NSVD1 (with and without Batch) +- Most Popular +- Random +- gSVD++ +- Item-MSMF +- (E)CoRec +Clustering: +- PaCo: EntroPy Anomalies in Co-Clustering +- k-medoids +Evaluation and Validation Metrics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +- All-but-one Protocol +- Cross-fold- Validation +- Item Recommendation: Precision, Recall, NDCG and Map +- Rating Prediction: MAE and RMSE +- Statistical Analysis (T-test and Wilcoxon) +Requirements +^^^^^^^^^^^^^ +- Python >= 3 +- scipy +- numpy +- pandas +- scikit-learn +For Linux, Windows and MAC use: + $ pip install requirements +For Windows libraries help use: + http://www.lfd.uci.edu/~gohlke/pythonlibs/ +Quick Start and Guide +^^^^^^^^^^^^^^^^^^^^^^ +For more information about RiVal and the documentation, +visit the Case Recommender +`Wiki `_. If you have not used Case Recommender before, do check out the Getting Started guide. +Installation +^^^^^^^^^^^^^ +Case Recommender can be installed using pip: + $ pip install caserecommender +If you want to run the latest version of the code, you can install from git: + $ pip install -U git+git://github.com/caserec/CaseRecommender.git +More Details +^^^^^^^^^^^^^ + `https://github.com/caserec/CaseRecommender `_ +License (MIT) +^^^^^^^^^^^^^^ + © 2019. Case Recommender All Rights Reserved + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated + documentation files (the "Software"), to deal in the Software without restriction, including without limitation the + rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to the following conditions: + The above copyright notice and this permission notice shall be included in all copies or substantial portions + of the Software. + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED + TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL + THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION + OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + DEALINGS IN THE SOFTWARE. + +%prep +%autosetup -n CaseRecommender-1.1.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-CaseRecommender -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot - 1.1.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..968409b --- /dev/null +++ b/sources @@ -0,0 +1 @@ +70e1bf6ae8edd0b3d6d1a83a0ef11cf4 CaseRecommender-1.1.1.tar.gz -- cgit v1.2.3